Abstract
This paper considers the data analysis process as a chronology of action triplets in which both the first and the third actions of each single triplet require a human intervention and the in-between action requires a computational approach. This process is used for the investigation of time units where each unit can be described using a qualitative variable with either crisp or fuzzy features. Then, each time, a unit set is characterized via a transitional matrix (TM). If in most cases only one TM is plotted with the primary objective of generating a Markov process model featuring S behavior states, this paper therefore considers the case where several TMs are used, e.g. several individuals yield several matrices in an experimental design. To investigate the transition data sets, we suggest using Correspondence Analysis (CA) via an input table containing the values from these TMs, each row including the S*S values of each TM. In addition, several new TMs can be computed from the initial TM, for instance one overall table for each individual or for each experimental condition. Within CA, these additional TMs can be considered supplementary row-points to demonstrate to what extent individual behaviors are similar. To illustrate this use of CA, we consider data from 1) simulated signals and 2) a driving vigilance study during which eye movements were recorded and characterized though eye fixation TM with S fuzzy areas of interest.
The benefits and drawbacks of our approach are discussed and a comparison is performed with two hierarchical clustering processes used respectively with the transition matrices and modality pairs.